Following the tweet, I have been made aware of many excellent ressources. This issue just serves to collect them before I add them somewhere.
https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/ looks like a solid intro to linear modeling equivalent to the stats 101 models. Downsides: there is little visualization, and no mention of non-parametric (i think?), and a lot more sampling theory. Check if there are worked examples.
https://siminab.github.io/2018/01/10/everything-in-statistical-modeling-can-be-seen-as-a-regression/ contains the basics, but likely too superficial.
https://www.ncbi.nlm.nih.gov/pubmed/20063905 looks like an excellent academic discussion of rote learning vs. modeling.
Following the tweet, I have been made aware of many excellent ressources. This issue just serves to collect them before I add them somewhere.
https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/ looks like a solid intro to linear modeling equivalent to the stats 101 models. Downsides: there is little visualization, and no mention of non-parametric (i think?), and a lot more sampling theory. Check if there are worked examples.
https://siminab.github.io/2018/01/10/everything-in-statistical-modeling-can-be-seen-as-a-regression/ contains the basics, but likely too superficial.
https://www.ncbi.nlm.nih.gov/pubmed/20063905 looks like an excellent academic discussion of rote learning vs. modeling.